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Table 1 Description of categorical variables

From: Using artificial intelligence to reduce diagnostic workload without compromising detection of urinary tract infections

 

n

Proportion of entire dataset (%)

Incidence of significant bacterial growth (%)

Variance

Positive culture

57,857

27·19

  

Negative culture

154,771

72·81

  

Patient groups

 Persistent/recurrent infection

47,348

22·28

37·68

0·17

 Pregnant

28,222

13·28

7·16

0·12

 Renal inpatient/outpatient

11,755

5·55

26·20

0·05

 Pre-operative patient

9463

4·45

21·84

0·04

 Acute kidney disease

3891

1·83

31·23

0·02

 Immunocompromised

2114

0·66

23·18

0·01

 Multiple Sclerosis

1046

0·49

24·38

0·005

 Inpatient

43,349

20·40

20·81

0·16

 Positive for nitrates

5895

2·80

59·73

0·03

 Offensive smell

270

0·10

55·19

0·001

 Pyuria, no RBCs

24,587

11·60

52·27

0·10

 Haematuria, no WBCs

368

0·002

0·06

0·002

Age

  < 11 years old

14,594

6·87

17·23

 

Gender

 Males

54,070

25·40

21·58

 

 Females (total)

158,422

74·60

26·76

 

 Females (not pregnant)

130,200

61·29

33·85